Mean-Shift PCA by Knockoff Mean
Removing noise is difficult, but adding noise is easy. In this work, we show how to eliminate mean-shift noisy components from PCA by deliberately introducing knockoff mean-shift perturbation. Standard PCA is highly sensitive to shifts in the sample mean: a small fraction of samples from a shifted distribution can cause large deviations in the leading principal components. In high-dimensional regimes, existing Robust PCA approaches cannot handle the mean-shift contamination structure inherent in the mixture model. Using tools from Random Matrix Theory, we prove that the mean-shift spikes are spectrally separable from the stable eigenvalues of the original covariance. Furthermore, the orig...
| Authors | Mengda Li, Zeng Li, Jianfeng Yao |
|---|---|
| Primary category | stat.ML |
| Published | 25 May 2026 |
| Last updated at source | 25 May 2026 |
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